class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
code: def __init__(
      self,
      rescale_with_mean=True,
      rescale_with_std=True,
      n_components=2,
      n_iter=3,
      copy=True,
      check_input=True,
      random_state=None,
      engine="sklearn",
  ):
      self.n_components = n_components
      self.n_iter = n_iter
      self.rescale_with_mean = rescale_with_mean
      self.rescale_with_std = rescale_with_std
      self.copy = copy
      self.check_input = check_input
      self.random_state = random_state
      self.engine = engine
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 1 to create a new PCA object from inherited base classes: sklearn.base, sklearn.base, utils with example: obj = PCA(rescale_with_mean, rescale_with_std, n_components, n_iter, copy, check_input, random_state, engine)